For safe navigation around pedestrians, automated vehicles (AVs) need to plan their motion by accurately predicting pedestrians trajectories over long time horizons. Current approaches to AV motion planning around crosswalks predict only for short time horizons (1-2 s) and are based on data from pedestrian interactions with human-driven vehicles (HDVs). In this paper, we develop a hybrid systems model that uses pedestrians gap acceptance behavior and constant velocity dynamics for long-term pedestrian trajectory prediction when interacting with AVs. Results demonstrate the applicability of the model for long-term (> 5 s) pedestrian trajectory prediction at crosswalks. Further we compared measures of pedestrian crossing behaviors in the immersive virtual environment (when interacting with AVs) to that in the real world (results of published studies of pedestrians interacting with HDVs), and found similarities between the two. These similarities demonstrate the applicability of the hybrid model of AV interactions developed from an immersive virtual environment (IVE) for real-world scenarios for both AVs and HDVs.

中文翻译：

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自动车辆交互过程中行人人行横道行为的分析与预测

为了在行人周围安全导航，自动驾驶汽车 (AV) 需要通过准确预测行人长期轨迹来规划其运动。当前围绕人行横道进行 AV 运动规划的方法仅针对短时间范围（1-2 秒）进行预测，并且基于行人与人类驾驶车辆 (HDV) 的交互数据。在本文中，我们开发了一种混合系统模型，该模型在与 AV 交互时使用行人间隙接受行为和恒速动力学进行长期行人轨迹预测。结果表明该模型适用于人行横道处的长期（> 5 s）行人轨迹预测。此外，我们将沉浸式虚拟环境（与 AV 交互时）中的行人过路行为测量与现实世界中的行为（行人与 HDV 交互的已发表研究结果）进行了比较，并发现了两者之间的相似之处。这些相似之处证明了从沉浸式虚拟环境 (IVE) 开发的 AV 交互混合模型对于 AV 和 HDV 的真实世界场景的适用性。